Name
Title | ||
|---|---|---|
Particle swarm optimization-based support vector regression and Bayesian networks applied to the toxicity of organic compounds to tadpoles (Rana japonica) |
Abstract | ||
|---|---|---|
Particle swarm optimization (PSO) is a new optimization method with strong global search capability. In present work, PSO-support vector regression (SVR) model was proposed to predict the toxicity of organic compounds to tadpoles (Rana japonica), in which PSO was used to determine free parameters of SVR. These results showed that the prediction accuracy of PSO-SVR model is higher than those mode of MLR and PLS. Moreover, Bayesian networks (BNs) was adopted to describe the relationship between toxicity associated with molecular descriptors in this work. The result of BNs was considered to be reasonable. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/BMEI.2011.6098692 | 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI) |
Keywords | Field | DocType |
Rana japonica tadpoles,toxicity,Particle swarm optimization,support vector regression,Bayesian networks | Molecular descriptor,Particle swarm optimization,Pattern recognition,Regression,Rana japonica,Regression analysis,Computer science,Support vector machine,Bayesian network,Artificial intelligence,Free parameter | Conference |
Volume | Issue | ISSN |
4 | null | 1948-2914 |
ISBN | Citations | PageRank |
978-1-4244-9351-7 | 0 | 0.34 |
References | Authors | |
4 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiang Su | 1 | 2 | 1.17 |
| Wen-Cong Lu | 2 | 65 | 4.40 |
| Xu Liu | 3 | 0 | 0.34 |
| Tian Hong Gu | 4 | 0 | 0.68 |
| Bing Niu | 5 | 2 | 0.74 |